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All issues Volume 683 (2026) E3S Web Conf., 683 (2026) 01011 References
Open Access
Issue
E3S Web Conf.
Volume 683, 2026
2025 2nd International Conference on Environment Engineering, Urban Planning and Design (EEUPD 2025)
Article Number 01011
Number of page(s) 10
Section Urban Planning and Spatial Governance
DOI https://doi.org/10.1051/e3sconf/202668301011
Published online 09 January 2026
  1. The State Council of the People’s Republic of China. Modern comprehensive transportation system for 14th Five-Year Plan (State Council Document 〔2021〕No. 27) [EB/OL]. (2022-01-18) [2024-08-28]. [Google Scholar]
  2. Tong Z, An R, Liu Y. Impact of the built environment on residents’ commuting mode choices: A case study of urban village in Wuhan city[J]. Progress in Geography, 2021, 40(12): 2048-2060. [Google Scholar]
  3. Chen B. Impact of the built environment on residents’ multi-purpose commuting behavior on weekdays: An empirical study based on Guangzhou central urban area[J]. Urban Planning Forum, 2021, 40(12): 2048-2060. [Google Scholar]
  4. Chen L, Wang J, Li Q. Research on decision-making of elderly people’s public transport travel considering multi-factors[J]. Journal of Railway Science and Engineering, 2022, 19(02): 367-375. [Google Scholar]
  5. Paez A, Scott D, Potoglou D, et al. Elderly mobility: Demographic and spatial analysis of trip making in the Hamilton CMA, Canada[J]. Urban Studies, 2007, 44(1): 123-146. [Google Scholar]
  6. Abraham J, Hunt J. Specification and estimation of nested logit model of home, workplaces, and commuter mode choices by multiple-worker households[J]. Transportation Research Record Journal of the Transportation Research Board, 1997, 1606: 17-24. [Google Scholar]
  7. Gadepalli R, Tiwari G, Bolia N. Role of user’s socio-economic and travel characteristics in mode choice between city bus and informal transit services: Lessons from household surveys in Visakhapatnam, India[J]. Journal of Transport Geography, 2020, 88. [Google Scholar]
  8. Fan A. Impact of transport interventions on mode choice and travel perception considering heterogeneity[D]. Beijing Jiaotong University, 2020. [Google Scholar]
  9. Wang L. Study on the price discount strategy of urban public transport based on nested-logit model[J]. Journal of Xihua University (Natural Science Edition), 2015, 34(04): 59-64. [Google Scholar]
  10. Hu S, Weng J, et al. Impact of travelers’ dependence on public transportation based on extended theory of planned behavior[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(05): 1037-1044. [Google Scholar]
  11. Shin E, Inbakaran C. Demographics and transport choices of new households on Melbourne’s urban fringe[C]// Australasian Transport Research Forum (ATRF), 33rd, 2010, Canberra, ACT, Australia;, 2010: 33. [Google Scholar]
  12. Zhao P, Kong L. The impact of TOD on residents’ commuting activities: A case of Beijing[J]. Human Geography, 2017, 32(05): 125-131. [Google Scholar]
  13. Xie S. Research on the multi-scale relationship between built environment and residents’ spatial behavior[D]. Harbin Institute of Technology, 2019. [Google Scholar]
  14. Liu K. Relationship between urban elderly public transport travel and built environment: Based on Shiyan IC card data[C]//China Urban Planning Society, Chengdu Municipal People’s Government. Spatial Governance for High-Quality Development—Proceedings of 2021 China Urban Planning Annual Conference (06 Urban Transportation Planning). Huazhong University of Science and Technology;, 2021: 552-560. [Google Scholar]
  15. Gao Y, Cui J, Cheng J, et al. Suburban travel mode choice considering transit accessibility of stops in public transport [J]. Journal of Southeast University (English Edition), 2021, 37(02): 222-226. [Google Scholar]
  16. Shi F, Ju Y. Analysis on influencing factors of public transportation share: An empirical study of central Nanjing[J]. City Planning Review, 2015, 39(02): 76-84. [Google Scholar]
  17. Sun B, Dan B. The impact of urban built environment on commuting mode choice in Shanghai[J]. Acta Geographica Sinica, 2015, 70(10): 1664-1674. [Google Scholar]
  18. Guo Y, Zhang Z, Chen L, et al. Impact of urban built environment on commuting mode choices from the residential self-selection perspective[J]. Arid Land Geography, 2024, 47 (2): 307-318. [Google Scholar]
  19. Zhu J, Zhang Y, et al. Impact of urban built environment on commute mode choice by smart phone app data: A case study of Xi’ an city[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2021, 49 (02): 55-66. [Google Scholar]
  20. Liu J, Xiao L, et al. Non-Linear relationships between the built environment and walking to school: Applying extreme gradient boosting method[J]. Progress in Geography, 2022, 41(2): 251-263. [Google Scholar]
  21. Ewing R, Cervero R. Travel and the built environment[J]. Journal of the American Planning Association, 2010, 76(3): 265-294. [Google Scholar]
  22. Lin G. Research on traffic flow prediction model for Jinan city based on big data[J]. Intelligent City, 2024, 10(12): 20-22. [Google Scholar]
  23. Wang H. Study on the issues of informatization construction in urban public transport in Jinan city[D]. Shandong Normal University, 2020. [Google Scholar]
  24. Yu Y, Jiang Y, Qiu N, et al. Exploring built environment factors on e-bike travel behavior in urban China: A case study of Jinan[J]. Frontiers in public health, 2022, 10: 1013421. [Google Scholar]
  25. Li Z, Tang J, Yu T, et al. Understanding the interplay among urban public transport modes: Spatial variation and built environment effects[J]. Journal of Cleaner Production, 2024, 479: 144038. [Google Scholar]
  26. Yu L. Research on the impact of urban village built environment on transportation[D]. Harbin Institute of Technology, 2021. [Google Scholar]
  27. Cervero R, Kockelman K. Travel demand and the 3Ds: Density, diversity, and design[J]. Transportation research part D: Transport and environment, 1997, 2(3): 199-219. [Google Scholar]

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